Control systems are used in various arts, such as mechanical systems, electrical systems, hydraulic systems, etc. For illustration, two examples of such systems are: a torsion system with controlled electric machine, reduction gear and angle transmission shaft for controlling robotic arm, and a pneumatic/hydraulic system with controlled electric pump, reduction valves system and a tube for controlling the robotic arm, etc. In fact, control systems may also be implemented over a machine-human arrangement, e.g., a human running on a treadmill, with the treadmill speed and elevation being controlled according to efforts exerted by the human. The load can be both passive (e.g., a drill in computer numerical control (CNC) device) and active (e.g., a human on a treadmill).
In this respect, the term “machine” or “engine” is generically used herein to describe an energy exchanger/energy converter, e.g., a controlled device which can be used both as a motor and/or as a generator. The motor uses current to produce velocity and moment, while generator uses velocity to produce current and voltage. Such energy converter can be described as two-parametric energy exchangers and provide power as product of two inter-related parameters. So, while the motor example uses velocity and moment, a fluid system, for example, may use flow rate and pressure. Importantly, the sensorless actuators relevant to this disclosure are those that can be characterized by two parameters. The term “current” in this respect, is a measure of some kind of energy flow, e.g., electric energy, chemical energy, etc. The multi-parametric energy exchanger is an extension of the two-parametric energy exchanger, as will be elaborated later in this disclosure.
The term transmission is used herein as a generic term applied to a transducer or a systems for transducing the energy produced by the energy exchanger. The transmission transduces a combination of values of the two parameters as output by the energy exchanger into another combination of values, which may or may not be the same as output by the energy exchanger. For example, the transmission may transduce some combination of velocity and moment into a different combination of velocity and moment. Transmission systems generally perform multiple functions, e.g., provide more moment at the expense of velocity or vice-versa via reduction gear, blocks system, valves, etc., and/or alter the geometry from lateral motion into rotation, rotation into lateral motion, change the angle of rotation, etc. The term “actuator,” on the other hand, refers to the coupled machine-transmission arrangement, along with the control-drive mechanism.
To illustrate, the description proceeds with respect to electrical systems having an actuator comprising a motor shaft coupled to a transmission; however, the concept can be applied to other actuator systems as well. Control systems typically control the machine via a sensor positioned on a shaft between the machine and transmission system. Since in many applications it is crucial to control the moment and velocity applied to the load (i.e., moment and velocity on the transmission shaft), a constant mathematical model of the transmission is used and control is implemented on the machine shaft according to the model. However, performing control on the machine shaft poses certain limitations, including: the inconsistence of a physical transmission system with its model; time delays of the transmission system; dynamic changes in the transmission system and the load are inseparable; and, malfunctions are difficult to discover and correct.
To solve these limitations, an additional control is typically established based on a sensor positioned on the transmission shaft. The resulting control system is complex and hard to control due to the multitude of sensor inputs. Multiple sensor implementation also has limitations, including: price of the sensors; expensive control computations; slow control speed due to system complexity; hard to take corrective steps in case of malfunction due to system complexity. Moreover, in some situations there is a need to control the moment and/or velocity at the load, i.e., at the transmission shaft, but the conditions or design of the system do not enable placing a sensors on the transmission shaft.
FIG. 1 presents a typical design of a conventional system based on multiple sensors. Block 101, the controller, generates a signal that controls the driver 102. The signal issued by the controller 101 corresponds to a digital command from computational device 106. Driver 102 translates the signal of the controller 101 into a current that drives the machine (e.g., electrical motor, hydraulic pump, etc.) 103. Machine 103 generates velocity and moment at its output, as a result of the current it receives from the driver 102. Transmission 104 is coupled to the machine 103 via coupling 109, in this example, the transmission is connected to the motor via motor shaft 109. The transmission transduces the velocity and moment of the machine shaft 109 into different velocity and moment on the transmission shaft 110. Load 105 is subjected to the velocity and moment it receives through the transmission shaft 110. Generally, machine shaft 109 provides mechanical, hydraulic, etc., coupling between the machine 103 and the transmission 104, while transmission shaft 110 provides mechanical, hydraulic, etc., coupling between the transmission 104 and the load 105.
Computational device 106 receives its data from the sensors 107 and 108, executes calibration and control algorithms, and sends digital command to the controller 101. Velocity sensor 107 is positioned on the machine shaft 109. Sensor 107 gathers data regarding the velocity of the machine shaft 109 and sends information to the computational device 106. Sensor 108 is positioned on the transmission shaft 110, and is especially beneficial when the transmission ratio is changed significantly during operation.
FIG. 2 presents a typical design of a conventional control loop. The current 201 is the input of the control loop. The velocity and the moment on the load shaft 202 are the output of the control loop. The transfer function F_V1 (s) in block 203, models the velocity at the output of the machine shaft 109 as a function of the current 201. The velocity sensor 107 measures the actual velocity at the output of the machine shaft, which may differ from the velocity modeled by F_V1. The transfer function F_V2 (s) in block 204, models the velocity at the output of the transmission shaft 110 as a function of the velocity of the machine shaft 109. The velocity and moment sensor 108 measures the actual velocity at the output of the transmission shaft, which may differ from the velocity modeled by F_V2. The transfer function F_M2 (s) in block 205, models the moment at the output of the transmission shaft 110 as a function of the velocity of the transmission shaft 110. The velocity and moment sensor 108 measures the actual moment at the output of the transmission shaft, which may differ from the moment modeled by F_M2.
The control loop is closed via computation of three transfer functions, in order to reconcile the modeled and the actually measured parameters. Each of these functions is complex and requires extensive computations. The transfer function G_V1 (s) in block 206 closes the loop between the velocity at the output of the machine shaft 109 and the current 201. The transfer function G_V2 (s) in block 207 closes the loop between the velocity at the output of the transmission shaft 110 and the current 201. The transfer function G_M3 (s) in block 208 closes the loop between the moment at the output of the transmission shaft 110 and the current 201.
Generally, computational device 106 executes complex calculations to provide feedback that incorporates velocity measurement of sensor 107, and moment and velocity measurements of sensor 108. This leads to higher costs and lower reliability and response-time of the control system. Notably, since the control system attempts to correct for three independently measured parameters, the response time is sufficiently large that secondary and higher order effects become significant and makes precise control more difficult. Accordingly, it would be beneficial to provide a solution that enables simple and fast control, yet avoids the disadvantages associated with conventional control systems.
Controlling multiple parameters is a challenging problem in control systems. There are multiple conventional methods with inherent problems. The artificial intelligence methods, such as neural networks and fuzzy-logic, attempt to control the parameters in a form similar to human behavior in similar situation. The behavior of various parameters of the control system is reduced to complex combinations of more simple functions, and the algorithms are trained to achieve the behavior programmed by the designer. The construction and training process of these algorithms is both science and art, since the algorithms have to be selected and optimized for each specific problem. The behavior of the system controlled via artificial intelligence in abnormal situations can be unpredictable and unstable. These methods are usually applicable when there are sensors to give a feed back and provide a closed loop.
Another conventional method is prediction matrix. Prediction matrix methods are based on connecting all parameters and their derivatives via mathematical matrix. The future behavior of the system is predicted by analyzing the current behavior, and the prediction is later verified. The difference between the predicted and the measured value is called innovation. The innovation is used to adapt the system to changing environment. The mathematical design of these schemes is based on variations of the Kalman filter (sometimes referred to as linear quadratic estimation—LQE), including nested Kalman filters. These methods commonly experience the difficulty of adaptation based on innovation, since it is hard to attribute innovation to any one or several changes in measured parameters.
The off-line periodic method includes testing of different system parameters periodically, using system off-line internal and/or external instruments, when integration of on-line real time sensors is not technologically and/or economically effective. Real time controllability is provided at the actuator outputs (typically stations/centers) rather than at the system interface/delivery points. This loss of information and regulation ability at the system level (low testability and controllability of this current method) is follow by high risk factors and difficult decision process (decision is taken based on statistic methods with different risk factors) and results in low system effectiveness.
As such, new systems and methods are needed to manage energy balance between various components of an energy exchange platform to obviate the shortcomings of the current platforms. Furthermore, what is needed is to facilitate the energy exchange by designing intelligent control system that can dynamically control the flow of energy.